Analysis of Hand De-occlusion and Removal (HDR) Module.
There are two major challenges of hand pose estimation in interacting scenarios, i.e. severe self-occlusion, and ambiguity caused by the homogeneous appearance of hands. As shown in Table REF , #1, #2, #3, and #8, we conduct ablative experiments to quantitatively evaluate the effect of De-occlusion and Removal. Comparing #2 and #8, we observe that disabling `Removal' will dramatically increase the MPJPE by 34.4%. Comparing #3 and #8, we see that disabling `De-occlusion' increases the MPJPE by 9.5%. If we only apply SHPE [1]} without HDR, the errors are further increased. These results clearly show that (1) state-of-the-art SHPE [1]} is sensitive to self-occlusions and inter-hand ambiguities (2) HDRM is effective in handling the aforementioned two major challenges.
